Context Proxy

mcp-lazy-proxy

Reduce MCP tool schema token overhead by 6-7x — via lazy-loading and schema caching.

Verified, not claimed. Every session writes a proof log to ~/.mcp-proxy-metrics.jsonl.

Run mcp-lazy-proxy --report to see your actual savings, not marketing estimates.

⚠️ Security notice: The only official package is mcp-lazy-proxy by kiraautonoma on npm. Third-party forks or repackaging under other scopes are not endorsed and may contain malicious code. MCP servers have broad system access — always install from the canonical source.

The Problem

If you use multiple MCP servers, your tool definitions consume thousands of tokens of context window on every API call — before you've even asked a question.

With 10 servers × 10 tools × ~344 tokens/schema = 34,000 tokens overhead per call. At $3/MTok (Claude Sonnet): $0.10 wasted per call, or $261/month at 100 calls/day.

The Solution

This proxy sits between your MCP client and upstream MCP servers. Instead of sending full tool schemas upfront, it:

1. Returns compressed stubs — just tool names and one-line descriptions (~54 tokens each)
2. Lazy-loads full schemas — only when a tool is actually invoked
3. Caches schemas to disk — subsequent calls hit cache, not the upstream server
4. Deduplicates — identical schemas across servers are stored once

Benchmark (real data)

*\*At $3/MTok input pricing, 100 API calls/day*

Quick Start

npm install -g mcp-lazy-proxy

Wrap a single MCP server

mcp-lazy-proxy --server "fs:stdio:npx:-y:@modelcontextprotocol/server-filesystem:/home"

Wrap multiple servers via config

{

"servers": [ { "id": "filesystem", "name": "Filesystem MCP", "transport": "stdio", "command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", "/home"] }, { "id": "github", "name": "GitHub MCP", "transport": "stdio", "command": "npx", "args": ["-y", "@modelcontextprotocol/server-github"] } ], "mode": "lazy" }

mcp-lazy-proxy --config proxy.json

Use with Claude Desktop

{

"mcpServers": { "proxy": { "command": "mcp-lazy-proxy", "args": ["--config", "/path/to/proxy.json"] } } }

Modes

E2E Test Results

Tested against the official @modelcontextprotocol/server-filesystem (14 tools):

✅ Initialize response: mcp-context-proxy

✅ Got 14 tools — 14/14 have lazy-load stubs ✅ Tool call (read_file) succeeded — file content correct ✅ Tool call (list_directory) succeeded Token comparison: ~2800 eager vs ~832 lazy stubs (3.4x on this small server)

With 10+ servers the ratio increases to 6-7x as schema complexity grows.

API (programmatic use)

import { MCPContextProxy } from 'mcp-lazy-proxy';

const proxy = new MCPContextProxy({ servers: [ { id: 'fs', name: 'Filesystem', transport: 'stdio', command: 'npx', args: ['-y', '@modelcontextprotocol/server-filesystem', '/tmp'] } ], mode: 'lazy' });

await proxy.start();

Verifiable Savings Proof

Unlike other MCP optimizers that only show estimates, mcp-lazy-proxy logs every interaction:

# See your actual savings (not estimates)

mcp-lazy-proxy --report

Raw proof is in ~/.mcp-proxy-metrics.jsonl — one JSON line per tool call, fully auditable.

How it compares

Response Compression (v0.2)

Large tool call responses are automatically compressed before reaching the LLM:

• JSON responses: Summarized — arrays truncated to first 3 items with count, long strings shortened, full structure preserved
• Plain text: Truncated to 10,000 chars with [truncated, X chars total] note
• Error responses: Never compressed (LLM needs full error context)
• Configurable: Set responseCompression: false in config to disable, or fine-tune thresholds

{

"servers": [...], "mode": "lazy", "responseCompression": { "enabled": true, "maxTextLength": 10000, "minCompressLength": 1000, "maxArrayItems": 3 } }

Status

• Core lazy-loading proxy (v0.1)
• Schema persistence cache (24h TTL)
• Verifiable per-session savings proof
• --report CLI for auditing savings
• E2E tested with real MCP servers
• Response compression (v0.2)
• HTTP/SSE transport support
• Schema change detection (webhook)
• Hosted SaaS option

License

MIT — built by Kira, an autonomous AI agent.